构建企业人力资本披露词库，助力量化分析人力资本管理。
Measuring Corporate Human Capital Disclosures: Lexicon, Data, Code, and Research Opportunities
- 用word2vec算法训练出五类人力资本关键词
- 提供完整词库、数据与Python代码开源
- 适合研究人力资本披露或企业沟通的学者使用
人力资本（HC）对企业价值创造日益重要，但目前缺乏明确的衡量与披露规范。本文利用经过验证的人力资本披露文本，通过word2vec机器学习算法，构建了一个涵盖五个子类别（多样性、公平性与包容性；健康与安全；劳资关系与企业文化；薪酬与福利；人口统计学及其他）的全面关键词列表，以捕捉人力资本管理的多维度特征。我们公开了该词库、企业人力资本披露数据以及生成词库所用的Python代码，并提供了详细的应用示例，包括对BERT模型进行微调的方法。研究人员可将本词库用于自身样本的企业沟通文本分析，以回答相关人力资本问题。文章最后探讨了未来在人力资本管理与披露方面的研究机会。
原文摘要 · Abstract (English)
Human capital (HC) is increasingly important to corporate value creation. Unlike other assets, however, HC is not currently subject to well-defined measurement or disclosure rules. We use a machine learning algorithm (word2vec) trained on a confirmed set of HC disclosures to develop a comprehensive list of HC-related keywords classified into five subcategories (DEI; health and safety; labor relations and culture; compensation and benefits; and demographics and other) that capture the multidimensional nature of HC management. We share our lexicon, corporate HC disclosures, and the Python code used to develop the lexicon, and we provide detailed examples of using our data and code, including for fine-tuning a BERT model. Researchers can use our HC lexicon (or modify the code to capture another construct of interest) with their samples of corporate communications to address pertinent HC questions. We close with a discussion of future research opportunities related to HC management and disclosure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。